1. Property Prediction and CAMD CHEN 4470 – Process Design Practice Dr. Mario Richard Eden Department of Chemical Engineering Auburn University Lecture No. 13 – Property Prediction and Computer Aided Molecular Design February 21, 2008

2. Property Prediction 1:2 Motivation –Experiments are time-consuming and expensive. –How do we identify the components to investigate? –Components of similar molecular structure have been found to have similar properties. Group Contribution Methods –Predominant means of predicting physical properties for new components. –Based on UNIFAC group descriptions –Large amounts of experimental property data has been fitted to obtain the contributions of individual groups.

3. Property Prediction 2:2 Examples and Software

4. CAMD 1:3

5. CAMD 2:3

6. CAMD 3:3 Application Examples –Water/phenol system: Toluene replacement –Separation of Cyclohexane and Benzene –Separation of Acetone and Chloroform –Refrigerants for heat pump systems –Heat transfer fluids for heat recovery and storage –and many others

7. Aniline Case Study 1:7 Problem Description –During the production of a pharmaceutical, aniline is formed as a byproduct. Due to strict product specifications the aniline content of an aqueous solution has to be reduced from 28000 ppm to 2 ppm. Conventional Approach –Single stage distillation. –Reduces aniline content to 500 ppm. –Energy usage: 4248.7 MJ –No data is available for the subsequent downstream processing steps.

8. Aniline Case Study 2:7 Objective –Investigate the possibility of using liquid-liquid extraction as an alternative unit operation by identification of a feasible solvent Reported Aniline Solvents –Water, Methanol, Ethanol, Ethyl Acetate, Acetone

9. Performance of Solvent –Liquid at ambient temperature –Immiscible with water –No azeotropes between solvent & aniline and/or water –High selectivity with respect to aniline –Minimal solvent loss to water phase –Sufficient difference in boiling points for recovery Structural and EH&S Aspects –No phenols, amines, amides or polyfunctional compounds. –No compounds containing double/triple bonds. –No compounds containing Si, F, Cl, Br, I or S Aniline Case Study 3:7

10. Aniline Case Study 4:7 Results of Solvent Search –No high boiling solvents found Also, higher and branched alkanes were identified as candidates

11. Aniline Case Study 5:7 Process Simulation

12. Aniline Case Study 6:7 Performance Targets and Results –Countercurrent extraction and simple distillation. –Terminal concentration of 2 ppm aniline in water phase. –Highest possible purity during solvent regeneration

13. Aniline Case Study 7:7 Validation of Minimum Cost Solution

14. Oleic Acid Methyl Ester 1:3 Problem Description –Fatty acid used in a variety of applications, e.g. textile treatment, rubbers, waxes, and biochemical research –Reported solvents: Diethyl Ether, Chloroform Goal –Identify alternative solvents with better safety and environmental properties. Volatile Flammable Carcinogen

15. Oleic Acid Methyl Ester 2:3 Solvent Specification –Liquid at normal (ambient) operating conditions. –Non-aromatic and non-acidic (stability of ester). –Good solvent for Oleic acid methyl ester. Constraints –Melting Point (T m ) < 280K –Boiling Point (T b ) > 340K –Acyclic compounds containing no Cl, Br, F, N or S –Octanol/Water Partition coefficient (logP) < 2 –15.95 (MPa) ½ < δ < 17.95 (MPa) ½

16. Oleic Acid Methyl Ester 3:3 Database Approach (2 Candidates) –2-Heptanone –Diethyl Carbitol CAMD Approach (1351 Compounds Found) –Maximum of two functional groups allowed, thus avoiding complex (and expensive) compounds. –Formic acid 2,3-dimethyl-butyl ester –3-Ethoxy-2-methyl-butyraldehyde –2-Ethoxy-3-methyl-butyraldehyde –Calculation time approximately 45 sec on standard PC.

17. Why Design Based on Properties? –Many processes driven by properties NOT components –Performance objectives often described by properties –Often objectives can not be described by composition –Product/molecular design is based on properties –Insights hidden by not integrating properties directly Property Clusters –Extension to existing composition based methods –Reduces dimensionality of problem –Enables visualization of problem –Property estimation in molecular design via GC –Unifying framework for simultaneous solution Property Based Design

18. Property clusters are conserved surrogate properties described by property operators, which have linear mixing rules, even if the operators themselves are nonlinear. Property Clusters 1:2 R.H.S.

19. Property Clusters 2:2 Feed Constraint Feasibility Region Analysis has shown that region boundary can be described by 6 unique points. Feasibility Necessary Condition Match clustering target Sufficient Condition Match AUP value of sink

20. Group Contribution Methods (GCM) allow for prediction of physical properties from structural information 1 st order, 2 nd order, and 3 rd order groups are utilized to increase the accuracy of the predicted properties Group Contribution Methods

21. Molecular Clusters 1:5

22. Molecular Clusters 2:5

23. 2 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 0,10,20,30,40,50,60,70,80,9 C3C3 C2C2 C1C1 M1 G1 G2 G3 G4 Feasibility Region  1, the visualization arm, corresponds to the location of G1-G2 molecule Molecular Clusters 3:5

24. 1: CH 3 2: CH 2 3: CH 3 N 4: COOH Molecular Synthesis CH 3 -(CH 2 ) 2 -CH 3 N-COOH Molecular Clusters 4:5

25. 1: CH 3 2: CH 2 3: CH 3 N 4: COOH 5: CH 3 N-COOH Molecular Synthesis CH 3 -(CH 2 ) 2 -CH 3 N-COOH Molecular Clusters 4:5

26. 1: CH 3 2: CH 2 3: CH 3 N 4: COOH 5: CH 3 N-COOH 6: CH 3 -CH 2 Molecular Synthesis CH 3 -(CH 2 ) 2 -CH 3 N-COOH Molecular Clusters 4:5

27. 1: CH 3 2: CH 2 3: CH 3 N 4: COOH 5: CH 3 N-COOH 6: CH 3 -CH 2 7: CH 3 -(CH 2 ) 2 Molecular Synthesis CH 3 -(CH 2 ) 2 -CH 3 N-COOH Molecular Clusters 4:5

28. 1: CH 3 2: CH 2 3: CH 3 N 4: COOH 5: CH 3 N-COOH 6: CH 3 -CH 2 7: CH 3 -(CH 2 ) 2 Molecular Synthesis CH 3 -(CH 2 ) 2 -CH 3 N-COOH Molecular Clusters 4:5

29. The location of each molecular formulation is unique and independent of group addition path Formulation of Butyl methyl ether CH 3 -CH 2 -CH 2 -CH 2 -CH 3 O Molecular Clusters 5:5

30. Blanket Wash Solvent Design –Solved as MINLP by Sinha and Achenie (2001) Problem Statement –Design blanket wash solvent for phenolic resin printing ink –Molecules designed from 7 possible groups, with a max. chain length of 7 groups Example: Molecular Synthesis PropertyLower BoundUpper Bound Hv (kJ/mol)2060 Tb (K)350400 Tm (K)150250 VP (mmHg)100--- R ij 019.8

31. Blanket Wash Solvent 1:7 Visualization limits problem to three properties Heat of vaporization, boiling and melting temperatures are used, with vapor pressure and solubility used as final screening properties Property Prediction (GCM)Molecular Property Operators  ref = 100  ref = 20  ref = 7

32. Blanket Wash Solvent 2:7

33. Blanket Wash Solvent 3:7

34. Blanket Wash Solvent 4:7

35. Application of feasibility conditions –All formulations satisfy the first two necessary conditions –M9-M11 fail to satisfy the AUP range of the sink Feasible formulations from Visual Synthesis Blanket Wash Solvent 5:7

36. Application of feasibility conditions –Checking property values with sink including Non-GC properties (VP, solubility), the sufficient conditions are satisfied for remaining formulations Feasible formulations from Visual Synthesis Blanket Wash Solvent 6:7

37. Candidate molecules M1-M7 identified visually by the developed method correspond to solutions found by the MINLP approach used by Sinha and Achenie (2001) Although valid formulation, heptane (M8) is flammable hence not an ideal solvent Cyclical compound Ethers MEK Blanket Wash Solvent 7:7

38. Integrated Design Approach Stream Properties & Unit Constraints Clusters Property Targets Clusters M Molecular Formulations Process Design Molecular Design Process/ Product Design Calculations Molecular Design

39. Example – Integrated Design Stream Characterization Sulfur Content (S) Molar Volume (Vm) Vapor Pressure (VP) Objective To maximize the use of off- gas condensate and to minimize fresh solvent use to the degrease

40. , S ref = 0.5 wt%, V m ref = 80 cm3/mol, VP ref = 760 mmHg Property Operator Mixing Rules Degreaser Feed Constraints PropertyLower BoundUpper Bound S (%)0.001.00 V m (cm 3 /mol)90.09487.80 VP (mmHg)15963040 Metal Degreasing 1:9

41. Visualization of Process Design Problem VOC Condensation Data Sulfur content, density and vapor pressure data given for temperature range 480K-515K Metal Degreasing 2:9

42. Point A & B dictate property constraint targets 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.10.20.30.40.50.60.70.80.9 C 3 C 2 C 1 505 K 500 K 495 K 490 K 485 K480 K 510 K 515 K DEGREASER CONDENSATE POINT A POINT B Conditions Condenser operates @ 500 K Feed Solvent must have zero sulfur content Visualization of Process Design Problem Metal Degreasing 3:9

43. Values from Process Design Visual Solution S (%) V m (cm 3 /mol) VP (mmHg) 0102.091825.37 0720.753878.66 H v (kJ/mol) V m (cm 3 /mol) VP (mmHg) 50102.091825.37 100720.753878.66 Molecular Property Constraints Metal Degreasing 4:9

44. Property Prediction (GCM) Non-GC Property Molecular Property Operators 7,  ref  100,  ref  20,  ref  Metal Degreasing 5:9

45. Molecular Fragments G1: CH 3 G2: CH 2 G3: CH 2 O G4: CH 2 N G5: CH 3 N G6: CH 3 CO G7: COOH Metal Degreasing 6:9 Visualization of Molecular Design Problem

46. Candidate Molecules M1 CH 3 -(CH 2 ) 5 -CH 3 CO M2 CH 3 CO-(CH 2 ) 2 -CH 3 CO M3 (CH 3 ) 3 -(CH 2 ) 5 -CH 2 N M4 CH 3 -(CH 2 ) 2 -COOH M5 (CH 3 ) 2 -CH 3 CO-CCL M6 -(CH2O) 5 - ring M7 CH 3 -(CH 2 ) 2 -CH 3 N-COOH Metal Degreasing 7:9 Visualization of Molecular Design Problem

47. Formulations from Visual Design FormulationAUPTb (K) Hv (kJ/mol) Vm (cm 3 /mol) VP (mmHg) M15.06450.5853.19156.852078.98 M24.71448.5454.13118.032163.90 M35.11437.2949.35189.412692.07 M44.86438.9763.2993.392606.12 M54.02413.2043.88121.144241.48 M64.19428.1144.22127.663208.12 M75.71485.0170.24112.521037.99 Application of Feasibility Conditions –All formulations satisfy first two necessary conditions –M5 and M6 fail to satisfy sink AUP range –M3 and M7 did not match Non-GC property value –M1, M2 and M4 are valid solvent candidates Metal Degreasing 8:9

48. Solutions to Molecular Design Problem Maximization of Condensate 17.44 kg/min of condensate recycle is utilized 19.36 kg/min of 2,5- hexadione as fresh solvent Metal Degreasing 9:9 Visualization of Process Design Solution

49. Property Prediction and CAMD –Can generate data for the simulation software in order to solve novel problems –Allows for development of environmentally benign designs and components –Systematic approaches, do not rely on rules of thumb. –Utilizes process/product knowledge at selection level. –Expands solution space for solvent design/selection –Capable of identifying novel compounds not included in databases and/or literature –Methodology has been proven through numerous application studies –Powerful tool when used in an integrated framework Summary

50. Next Lecture – February 26 –Product engineering and Six Sigma –SSL pp. 644-672 Other Business